<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml" lang="" xml:lang="">
  <head>
    <meta charset="utf-8" />
    <meta name="generator" content="pandoc" />
    <meta
      name="viewport"
      content="width=device-width, initial-scale=1.0, user-scalable=yes"
    />
    <title>working-w-jupyter-vscode</title>
    <style type="text/css">
      code {
        white-space: pre-wrap;
      }
      span.smallcaps {
        font-variant: small-caps;
      }
      span.underline {
        text-decoration: underline;
      }
      div.column {
        display: inline-block;
        vertical-align: top;
        width: 50%;
      }
    </style>
  </head>
  <body>
    <h1 id="working-with-jupyter-notebooks-in-visual-studio-code">
      Working with Jupyter Notebooks in Visual Studio Code
    </h1>
    <blockquote>
      <p>Working with Jupyter Notebooks in Visual Studio Code</p>
    </blockquote>
    <p>
      <a href="https://jupyter-notebook.readthedocs.io/en/latest/">Jupyter</a>
      (formerly IPython Notebook) is an open-source project that lets you easily
      combine Markdown text and executable Python source code on one canvas
      called a <strong>notebook</strong>. Visual Studio Code supports working
      with Jupyter Notebooks natively, as well as through
      <a
        href="chrome-extension://cjedbglnccaioiolemnfhjncicchinao/docs/python/jupyter-support-py"
        >Python code files</a
      >. This topic covers the native support available for Jupyter Notebooks
      and demonstrates how to:
    </p>
    <ul>
      <li>Create, open, and save Jupyter Notebooks</li>
      <li>Work with Jupyter code cells</li>
      <li>
        View, inspect, and filter variables using the Variable explorer and Data
        viewer
      </li>
      <li>Connect to a remote Jupyter server</li>
      <li>Debug a Jupyter notebook</li>
    </ul>
    <h2 id="setting-up-your-environment">
      Setting up your environment<a href="#_setting-up-your-environment">#</a>
    </h2>
    <p>
      To work with Jupyter notebooks, you must activate an Anaconda environment
      in VS Code, or another Python environment in which you’ve installed the
      <a href="https://pypi.org/project/jupyter/">Jupyter package</a>. To select
      an environment, use the
      <strong>Python: Select Interpreter</strong> command from the Command
      Palette (Ctrl+Shift+P).
    </p>
    <p>
      Once the appropriate environment is activated, you can create and open a
      Jupyter Notebook, connect to a remote Jupyter server for running code
      cells, and export a Jupyter Notebook as a Python file.
    </p>
    <blockquote>
      <p>
        <strong>Note:</strong> By default, the Visual Studio Code Python
        extension will open a Jupyter Notebook (.ipynb) in the Notebook Editor.
        If you want to disable this behavior you can turn it off in settings.
        (Python &gt; Data Science: Use Notebook Editor).
      </p>
    </blockquote>
    <h2 id="create-or-open-a-jupyter-notebook">
      Create or open a Jupyter Notebook<a
        href="#_create-or-open-a-jupyter-notebook"
        >#</a
      >
    </h2>
    <p>
      You can create a Jupyter Notebook by running the
      <strong>Jupyter: Create Blank New Jupyter Notebook</strong> command from
      the Command Palette (Ctrl+Shift+P) or by creating a new .ipynb file in
      your workspace. When you select the file, the Notebook Editor is launched
      allowing you to edit and run code cells.
    </p>
    <figure>
      <img
        src="chrome-extension://cjedbglnccaioiolemnfhjncicchinao/assets/docs/python/jupyter/native-code-cells-01.png"
        alt="Blank Jupyter Notebook"
      />
      <figcaption>Blank Jupyter Notebook</figcaption>
    </figure>
    <p>
      If you have an existing Jupyter Notebook, you can open it in the Notebook
      Editor by double-clicking on the file and opening with Visual Studio Code,
      through the Visual Studio Code, or using the Command Palette
      <strong>Jupyter: Open in Notebook Editor</strong> command.
    </p>
    <p>
      Once you have a Notebook created, you can run a code cell using the green
      run icon above the cell and the output will appear directly below the code
      cell.
    </p>
    <figure>
      <img
        src="chrome-extension://cjedbglnccaioiolemnfhjncicchinao/assets/docs/python/jupyter/native-code-cells-03.png"
        alt="Run Jupyter code cell"
      />
      <figcaption>Run Jupyter code cell</figcaption>
    </figure>
    <h2 id="trusted-notebooks">
      Trusted Notebooks<a href="#_trusted-notebooks">#</a>
    </h2>
    <p>
      It’s possible for malicious source code to be contained in a Jupyter
      Notebook. With that in mind, to help protect you, any Notebook that’s not
      created with VS Code on your local machine (or explicitly set to
      <strong>Trusted</strong> by you) is considered
      <strong>Not Trusted</strong>. When a Notebook is
      <strong>Not Trusted</strong>, VS Code will not render Markdown cells or
      display the output of code cells within the Notebook. Instead, just the
      source of Markdown and code cells will be shown. The Notebook is
      essentially in read-only mode, with toolbars disabled and no ability to
      edit the file, until you set it as <strong>Trusted</strong>.
    </p>
    <blockquote>
      <p>
        <strong>Note</strong>: Before setting a Notebook as
        <strong>Trusted</strong>, it is up to you to verify that the source code
        and Markdown are safe to run. VS Code does not perform any sanitizing of
        Markdown, it merely prevents it from being rendered until a Notebook is
        marked as <strong>Trusted</strong> to help protect you from malicious
        code.
      </p>
    </blockquote>
    <p>
      When you first open a Notebook that’s <strong>Not Trusted</strong>, the
      following notification prompt is displayed.
    </p>
    <figure>
      <img
        src="chrome-extension://cjedbglnccaioiolemnfhjncicchinao/assets/docs/python/jupyter/native-trusted-prompt.png"
        alt="Trusted Notebook prompt"
      />
      <figcaption>Trusted Notebook prompt</figcaption>
    </figure>
    <p>
      If you select <strong>Trust</strong>, the Notebook will be trusted going
      forward. If you opt not to trust the Notebook, then
      <strong>Not Trusted</strong> will be displayed in the toolbar and the
      Notebook will remain in a read-only state as described previously. If you
      select <strong>Trust all notebooks</strong>, you will be taken to
      settings, where you can specify that all Notebooks opened in VS Code be
      trusted. That means you will no longer be prompted to trust individual
      notebooks and harmful code could automatically run.
    </p>
    <figure>
      <img
        src="chrome-extension://cjedbglnccaioiolemnfhjncicchinao/assets/docs/python/jupyter/native-trust-status-bar.png"
        alt="Trust status in toolbar"
      />
      <figcaption>Trust status in toolbar</figcaption>
    </figure>
    <p>
      You can relaunch the trust notification prompt after reviewing the
      Notebook by clicking on the <strong>Not Trusted</strong> status.
    </p>
    <h2 id="save-your-jupyter-notebook">
      Save your Jupyter Notebook<a href="#_save-your-jupyter-notebook">#</a>
    </h2>
    <p>
      You can save your Jupyter Notebook using the keyboard combo Ctrl+S or
      through the save icon on the Notebook Editor toolbar.
    </p>
    <figure>
      <img
        src="chrome-extension://cjedbglnccaioiolemnfhjncicchinao/assets/docs/python/jupyter/native-toolbar-save.png"
        alt="Notebook Editor save icon"
      />
      <figcaption>Notebook Editor save icon</figcaption>
    </figure>
    <blockquote>
      <p>
        <strong>Note:</strong> At present, you must use the methods discussed
        above to save your Notebook. The <strong>File</strong>&gt;<strong
          >Save</strong
        >
        menu does not save your Notebook, just the toolbar icon or keyboard
        command.
      </p>
    </blockquote>
    <h2 id="export-your-jupyter-notebook">
      Export your Jupyter Notebook<a href="#_export-your-jupyter-notebook">#</a>
    </h2>
    <p>
      You can export a Jupyter Notebook as a Python file (.py), a PDF, or an
      HTML file. To export, just click the convert icon on the main toolbar.
      You’ll then be presented with file options from the Command Palette.
    </p>
    <figure>
      <img
        src="chrome-extension://cjedbglnccaioiolemnfhjncicchinao/assets/docs/python/jupyter/native-toolbar-convert.png"
        alt="Convert Jupyter Notebook to Python file"
      />
      <figcaption>Convert Jupyter Notebook to Python file</figcaption>
    </figure>
    <blockquote>
      <p>
        <strong>Note:</strong> For PDF export, you must have TeX installed. If
        you don’t, you will be notified that you need to install it when you
        select the PDF option. Also, be aware that if you have SVG-only output
        in your Notebook, they will not be displayed in the PDF. To have SVG
        graphics in a PDF, either ensure that your output includes a non-SVG
        image format or else you can first export to HTML and then save as PDF
        using your browser.
      </p>
    </blockquote>
    <h2 id="work-with-code-cells-in-the-notebook-editor">
      Work with code cells in the Notebook Editor<a
        href="#_work-with-code-cells-in-the-notebook-editor"
        >#</a
      >
    </h2>
    <p>
      The Notebook Editor makes it easy to create, edit, and run code cells
      within your Jupyter Notebook.
    </p>
    <h3 id="create-a-code-cell">
      Create a code cell<a href="#_create-a-code-cell">#</a>
    </h3>
    <p>
      By default, a blank Notebook will have an empty code cell for you to start
      with and an existing Notebook will place one at the bottom. Add your code
      to the empty code cell to get started.
    </p>
    <pre><code>msg = &quot;Hello world&quot;
print(msg)</code></pre>
    <figure>
      <img
        src="chrome-extension://cjedbglnccaioiolemnfhjncicchinao/assets/docs/python/jupyter/native-code-cells-02.png"
        alt="Simple Jupyter code cell"
      />
      <figcaption>Simple Jupyter code cell</figcaption>
    </figure>
    <h3 id="code-cell-modes">
      Code cell modes<a href="#_code-cell-modes">#</a>
    </h3>
    <p>
      While working with code cells a cell can be in three states, unselected,
      command mode, and edit mode. The current state of a cell is indicated by a
      vertical bar to the left of a code cell. When no bar is visible, the cell
      is unselected.
    </p>
    <figure>
      <img
        src="chrome-extension://cjedbglnccaioiolemnfhjncicchinao/assets/docs/python/jupyter/native-code-cells-02.png"
        alt="Unselected Jupyter code cell"
      />
      <figcaption>Unselected Jupyter code cell</figcaption>
    </figure>
    <p>
      An unselected cell isn’t editable, but you can hover over it to reveal
      additional cell specific toolbar options. These additional toolbar options
      appear directly below and to the left of the cell. You’ll also see when
      hovering over a cell that an empty vertical bar is present to the left.
    </p>
    <figure>
      <img
        src="chrome-extension://cjedbglnccaioiolemnfhjncicchinao/assets/docs/python/jupyter/native-code-cells-02a.png"
        alt="Simple Jupyter code cell being hovered over"
      />
      <figcaption>Simple Jupyter code cell being hovered over</figcaption>
    </figure>
    <p>
      When a cell is selected, it can be in two different modes. It can be in
      command mode or in edit mode. When the cell is in command mode, it can be
      operated on and accept keyboard commands. When the cell is in edit mode,
      the cell’s contents (code or Markdown) can be modified.
    </p>
    <p>
      When a cell is in command mode, the vertical bar to the left of the cell
      will be solid to indicate it’s selected.
    </p>
    <figure>
      <img
        src="chrome-extension://cjedbglnccaioiolemnfhjncicchinao/assets/docs/python/jupyter/native-code-cells-09.png"
        alt="Code cell in command mode"
      />
      <figcaption>Code cell in command mode</figcaption>
    </figure>
    <p>When you’re in edit mode, the vertical bar will have diagonal lines.</p>
    <figure>
      <img
        src="chrome-extension://cjedbglnccaioiolemnfhjncicchinao/assets/docs/python/jupyter/native-code-cells-10.png"
        alt="Code cell in edit mode"
      />
      <figcaption>Code cell in edit mode</figcaption>
    </figure>
    <p>
      To move from edit mode to command mode, press the ESC key. To move from
      command mode to edit mode, press the Enter key. You can also use the mouse
      to <strong>change the mode</strong> by clicking the vertical bar to the
      left of the cell or out of the code/Markdown region in the code cell.
    </p>
    <h3 id="add-additional-code-cells">
      Add additional code cells<a href="#_add-additional-code-cells">#</a>
    </h3>
    <p>
      Code cells can be added to a Notebook using the main toolbar, a code
      cell’s vertical toolbar, the add code cell icon at the bottom of the
      Notebook, the add code cell icon at the top of the Notebook (visible with
      hover), and through keyboard commands.
    </p>
    <figure>
      <img
        src="chrome-extension://cjedbglnccaioiolemnfhjncicchinao/assets/docs/python/jupyter/native-code-cells-07.png"
        alt="Add code cells"
      />
      <figcaption>Add code cells</figcaption>
    </figure>
    <p>
      Using the plus icon in the main toolbar will add a new cell directly below
      the currently selected cell. Using the add cell icons at the top and
      bottom of the Jupyter Notebook, will add a code cell at the top and bottom
      respectively. And using the add icon in the code cell’s toolbar, will add
      a new code cell directly below it.
    </p>
    <p>
      When a code cell is in command mode, the A key can be used to add a cell
      above and the B can be used to add a cell below the selected cell.
    </p>
    <h3 id="select-a-code-cell">
      Select a code cell<a href="#_select-a-code-cell">#</a>
    </h3>
    <p>
      The selected code cell can be changed using the mouse, the up/down arrow
      keys on the keyboard, and the J (down) and K (up) keys. To use the
      keyboard, the cell must be in command mode.
    </p>
    <h3 id="run-a-single-code-cell">
      Run a single code cell<a href="#_run-a-single-code-cell">#</a>
    </h3>
    <p>
      Once your code is added, you can run a cell using the green run arrow and
      the output will be displayed below the code cell.
    </p>
    <figure>
      <img
        src="chrome-extension://cjedbglnccaioiolemnfhjncicchinao/assets/docs/python/jupyter/native-code-cells-03.png"
        alt="Run Jupyter code cell"
      />
      <figcaption>Run Jupyter code cell</figcaption>
    </figure>
    <p>
      You can also use key combos to run a selected code cell. Ctrl+Enter runs
      the currently selected cell, Shift+Enter runs the currently selected cell
      and inserts a new cell immediately below (focus moves to new cell), and
      Alt+Enter runs the currently selected cell and inserts a new cell
      immediately below (focus remains on current cell). These keyboard combos
      can be used in both command and edit modes.
    </p>
    <h3 id="run-multiple-code-cells">
      Run multiple code cells<a href="#_run-multiple-code-cells">#</a>
    </h3>
    <p>
      Running multiple code cells can be accomplished in a number of ways. You
      can use the double arrow in the toolbar of the Notebook Editor to run all
      cells within the Notebook or the run icons with directional arrows to run
      all cells above or below the current code cell.
    </p>
    <figure>
      <img
        src="chrome-extension://cjedbglnccaioiolemnfhjncicchinao/assets/docs/python/jupyter/native-code-cells-04.png"
        alt="Run multiple code cells"
      />
      <figcaption>Run multiple code cells</figcaption>
    </figure>
    <h3 id="run-code-by-line">
      Run code by line<a href="#_run-code-by-line">#</a>
    </h3>
    <p>
      To help diagnose issues with your Notebook code, run-by-line lets you step
      through the code of a cell in a line-by-line fashion. While stepping
      through code you can view the state of variables at each step via the
      variable explorer or hover your mouse over variables to see data tips.
    </p>
    <p>
      To start a session, just click the run-by-line icon to the right of the
      run cell icon on the cell’s toolbar.
    </p>
    <figure>
      <img
        src="chrome-extension://cjedbglnccaioiolemnfhjncicchinao/assets/docs/python/jupyter/native-code-cells-11a.png"
        alt="Start run code cell by line"
      />
      <figcaption>Start run code cell by line</figcaption>
    </figure>
    <figure>
      <img
        src="chrome-extension://cjedbglnccaioiolemnfhjncicchinao/assets/docs/python/jupyter/native-code-cells-11.png"
        alt="Run code cell by line"
      />
      <figcaption>Run code cell by line</figcaption>
    </figure>
    <p>
      Once in a run-by-line session, you can run the currently highlighted line
      of code by pressing the icon again. To exit, just click the stop button
      next to the run-by-line icon in the cell.
    </p>
    <h3 id="move-a-code-cell">
      Move a code cell<a href="#_move-a-code-cell">#</a>
    </h3>
    <p>
      Moving code cells up or down within a Notebook can be accomplished using
      the vertical arrows beside each code cell. Hover over the code cell and
      then click the up arrow to move the cell up and the down arrow to move the
      cell down.
    </p>
    <figure>
      <img
        src="chrome-extension://cjedbglnccaioiolemnfhjncicchinao/assets/docs/python/jupyter/native-code-cells-05.png"
        alt="Move a code cell"
      />
      <figcaption>Move a code cell</figcaption>
    </figure>
    <h3 id="delete-a-code-cell">
      Delete a code cell<a href="#_delete-a-code-cell">#</a>
    </h3>
    <p>
      Deleting a code cell can be accomplished by hovering over a code cell and
      using the delete icon in the code cell toolbar or through the keyboard
      combo dd when the selected code cell is in command mode.
    </p>
    <figure>
      <img
        src="chrome-extension://cjedbglnccaioiolemnfhjncicchinao/assets/docs/python/jupyter/native-code-cells-06.png"
        alt="Delete a code cell"
      />
      <figcaption>Delete a code cell</figcaption>
    </figure>
    <h3 id="undo-your-last-change">
      Undo your last change<a href="#_undo-your-last-change">#</a>
    </h3>
    <p>
      You can use the z key to undo your previous change, for example, if you’ve
      made an accidental edit you can undo it to the previous correct state, or
      if you’ve deleted a cell accidentally you can recover it.
    </p>
    <h3 id="switch-between-code-and-markdown">
      Switch between code and Markdown<a
        href="#_switch-between-code-and-markdown"
        >#</a
      >
    </h3>
    <p>
      The Notebook Editor allows you to easily change code cells between
      Markdown and code. By default a code cell is set for code, but just click
      the Markdown icon (or the code icon, if Markdown was previously set) in
      the code cell’s toolbar to change it.
    </p>
    <figure>
      <img
        src="chrome-extension://cjedbglnccaioiolemnfhjncicchinao/assets/docs/python/jupyter/native-code-cells-08.png"
        alt="Markdown toolbar icon"
      />
      <figcaption>Markdown toolbar icon</figcaption>
    </figure>
    <p>
      Once Markdown is set, you can enter Markdown formatted content to the code
      cell. Once you select another cell or toggle out of the content selection,
      the Markdown content is rendered in the Notebook Editor.
    </p>
    <figure>
      <img
        src="chrome-extension://cjedbglnccaioiolemnfhjncicchinao/assets/docs/python/jupyter/native-markdown-03.png"
        alt="Raw Markdown displayed in code cell"
      />
      <figcaption>Raw Markdown displayed in code cell</figcaption>
    </figure>
    <figure>
      <img
        src="chrome-extension://cjedbglnccaioiolemnfhjncicchinao/assets/docs/python/jupyter/native-markdown-02.png"
        alt="Rendered Markdown displayed in code cell"
      />
      <figcaption>Rendered Markdown displayed in code cell</figcaption>
    </figure>
    <p>
      You can also use the keyboard to change the cell type. When a cell is
      selected and in command mode, the M key switches the cell type to Markdown
      and the Y key switches the cell type to code.
    </p>
    <h3 id="clear-output-or-restartinterrupt-the-kernel">
      Clear output or restart/interrupt the kernel<a
        href="#_clear-output-or-restartinterrupt-the-kernel"
        >#</a
      >
    </h3>
    <p>
      If you’d like to clear the code cell output or restart/interrupt the
      kernel, you can accomplish that using the main Notebook Editor toolbar.
    </p>
    <figure>
      <img
        src="chrome-extension://cjedbglnccaioiolemnfhjncicchinao/assets/docs/python/jupyter/native-toolbar-additional-commands.png"
        alt="Additional Notebook Editor toolbar commands"
      />
      <figcaption>Additional Notebook Editor toolbar commands</figcaption>
    </figure>
    <h3 id="enabledisable-line-numbers">
      Enable/Disable line numbers<a href="#_enabledisable-line-numbers">#</a>
    </h3>
    <p>
      You can enable or disable line numbering within a code cell using the L
      key.
    </p>
    <figure>
      <img
        src="chrome-extension://cjedbglnccaioiolemnfhjncicchinao/assets/docs/python/jupyter/native-line-number.png"
        alt="Line numbers enabled in code cell"
      />
      <figcaption>Line numbers enabled in code cell</figcaption>
    </figure>
    <h2 id="intellisense-support-in-the-jupyter-notebook-editor">
      IntelliSense support in the Jupyter Notebook Editor<a
        href="#_intellisense-support-in-the-jupyter-notebook-editor"
        >#</a
      >
    </h2>
    <p>
      The Python Jupyter Notebook Editor window has full IntelliSense – code
      completions, member lists, quick info for methods, and parameter hints.
      You can be just as productive typing in the Notebook Editor window as you
      are in the code editor.
    </p>
    <figure>
      <img
        src="chrome-extension://cjedbglnccaioiolemnfhjncicchinao/assets/docs/python/jupyter/native-intellisense.png"
        alt="IntelliSense support"
      />
      <figcaption>IntelliSense support</figcaption>
    </figure>
    <h2 id="variable-explorer-and-data-viewer">
      Variable explorer and data viewer<a
        href="#_variable-explorer-and-data-viewer"
        >#</a
      >
    </h2>
    <p>
      Within the Python Notebook Editor, it’s possible to view, inspect, and
      filter the variables within your current Jupyter session. By clicking the
      <strong>Variables</strong> icon in the top toolbar after running code and
      cells, you’ll see a list of the current variables, which will
      automatically update as variables are used in code.
    </p>
    <figure>
      <img
        src="chrome-extension://cjedbglnccaioiolemnfhjncicchinao/assets/docs/python/jupyter/native-variable-explorer.png"
        alt="Variable Explorer"
      />
      <figcaption>Variable Explorer</figcaption>
    </figure>
    <p>
      For additional information about your variables, you can also double-click
      on a row or use the <strong>Show variable in data viewer</strong> button
      next to the variable to see a more detailed view of a variable in the Data
      Viewer. Once open, you can filter the values by searching over the rows.
    </p>
    <figure>
      <img
        src="chrome-extension://cjedbglnccaioiolemnfhjncicchinao/assets/docs/python/jupyter/native-data-viewer.png"
        alt="Data Viewer"
      />
      <figcaption>Data Viewer</figcaption>
    </figure>
    <blockquote>
      <p>
        <strong>Note:</strong> Variable explorer is enabled by default, but can
        be turned off in settings (Python &gt; Data Science: Show Jupyter
        Variable Explorer).
      </p>
    </blockquote>
    <h2 id="plot-viewer">Plot viewer<a href="#_plot-viewer">#</a></h2>
    <p>
      The Plot Viewer gives you the ability to work more deeply with your plots.
      In the viewer you can pan, zoom, and navigate plots in the current
      session. You can also export plots to PDF, SVG, and PNG formats.
    </p>
    <p>
      Within the Notebook Editor window, double-click any plot to open it in the
      viewer, or select the plot viewer button on the upper left corner of the
      plot (visible on hover).
    </p>
    <figure>
      <img
        src="chrome-extension://cjedbglnccaioiolemnfhjncicchinao/assets/docs/python/jupyter/native-plot-viewer.png"
        alt="Plot Viewer icon in the Notebook Editor"
      />
      <figcaption>Plot Viewer icon in the Notebook Editor</figcaption>
    </figure>
    <figure>
      <img
        src="chrome-extension://cjedbglnccaioiolemnfhjncicchinao/assets/docs/python/jupyter/native-plot-viewer-02.png"
        alt="Plot Viewer with a selected plot"
      />
      <figcaption>Plot Viewer with a selected plot</figcaption>
    </figure>
    <blockquote>
      <p>
        <strong>Note:</strong> There is support for rendering plots created with
        <a href="https://matplotlib.org/">matplotlib</a> and
        <a href="https://altair-viz.github.io/index.html">Altair</a>.
      </p>
    </blockquote>
    <h2 id="debug-a-jupyter-notebook">
      Debug a Jupyter Notebook<a href="#_debug-a-jupyter-notebook">#</a>
    </h2>
    <p>
      If you need additional debug support in order to diagnose an issue in your
      code cells, you can export it as a Python file. Once exported as a Python
      file, the Visual Studio Code debugger lets you step through your code, set
      breakpoints, examine state, and analyze problems. Using the debugger is a
      helpful way to find and correct issues in notebook code. To debug your
      Python file:
    </p>
    <ol type="1">
      <li>
        In VS Code, if you haven’t already, activate a Python environment in
        which Jupyter is installed.
      </li>
      <li>
        <p>
          From your Jupyter Notebook (.ipynb) select the convert button in the
          main toolbar.
        </p>
        <figure>
          <img
            src="chrome-extension://cjedbglnccaioiolemnfhjncicchinao/assets/docs/python/jupyter/native-toolbar-convert.png"
            alt="Convert Jupyter Notebook to Python file"
          />
          <figcaption>Convert Jupyter Notebook to Python file</figcaption>
        </figure>
        <p>
          Once exported, you’ll have a .py file with your code that you can use
          for debugging.
        </p>
      </li>
      <li>
        <p>
          After saving the .py file, to start the debugger, use one of the
          following options:
        </p>
        <ul>
          <li>
            For the whole Notebook, open the Command Palette (Ctrl+Shift+P) and
            run the
            <strong
              >Python: Debug Current File in Python Interactive Window</strong
            >
            command.
          </li>
          <li>
            For an individual cell, use the
            <strong>Debug Cell</strong> adornment that appears above the cell.
            The debugger specifically starts on the code in that cell. By
            default, <strong>Debug Cell</strong> just steps into user code. If
            you want to step into non-user code, you need to uncheck
            <strong>Data Science: Debug Just My Code</strong> in the Python
            extension settings (Ctrl+,).
          </li>
        </ul>
      </li>
      <li>
        To familiarize yourself with the general debugging features of VS Code,
        such as inspecting variables, setting breakpoints, and other activities,
        review
        <a
          href="chrome-extension://cjedbglnccaioiolemnfhjncicchinao/docs/editor/debugging"
          >VS Code debugging</a
        >.
      </li>
      <li>
        As you find issues, stop the debugger, correct your code, save the file,
        and start the debugger again.
      </li>
      <li>
        <p>
          When you’re satisfied that all your code is correct, use the Python
          Interactive window to export the Python file as a Jupyter Notebook
          (.ipynb).
        </p>
      </li>
    </ol>
    <h2 id="connect-to-a-remote-jupyter-server">
      Connect to a remote Jupyter server<a
        href="#_connect-to-a-remote-jupyter-server"
        >#</a
      >
    </h2>
    <p>
      You can offload intensive computation in a Jupyter Notebook to other
      computers by connecting to a remote Jupyter server. Once connected, code
      cells run on the remote server rather than the local computer.
    </p>
    <p>To connect to a remote Jupyter server:</p>
    <ol type="1">
      <li>
        Run the
        <strong
          >Jupyter: Specify local or remote Jupyter server for
          connections</strong
        >
        command from the Command Palette (Ctrl+Shift+P).
      </li>
      <li>
        <p>
          When prompted to <strong>Pick how to connect to Jupyter</strong>,
          select
          <strong>Existing: Specify the URI of an existing server</strong>.
        </p>
        <figure>
          <img
            src="chrome-extension://cjedbglnccaioiolemnfhjncicchinao/assets/docs/python/jupyter/connect-to-existing.png"
            alt="Choose to connect to an existing server"
          />
          <figcaption>Choose to connect to an existing server</figcaption>
        </figure>
      </li>
      <li>
        <p>
          When prompted to <strong>Enter the URI of a Jupyter server</strong>,
          provide the server’s URI (hostname) with the authentication token
          included with a <code>?token=</code> URL parameter. (If you start the
          server in the VS Code terminal with an authentication token enabled,
          the URL with the token typically appears in the terminal output from
          where you can copy it.) Alternatively, you can specify a username and
          password after providing the URI.
        </p>
        <figure>
          <img
            src="chrome-extension://cjedbglnccaioiolemnfhjncicchinao/assets/docs/python/jupyter/enter-url-auth-token.png"
            alt="Prompt to supply a Jupyter server URI"
          />
          <figcaption>Prompt to supply a Jupyter server URI</figcaption>
        </figure>
      </li>
    </ol>
    <blockquote>
      <p>
        <strong>Note:</strong> For added security, Microsoft recommends
        configuring your Jupyter server with security precautions such as SSL
        and token support. This helps ensure that requests sent to the Jupyter
        server are authenticated and connections to the remoter server are
        encrypted. For guidance about securing a notebook server, see the
        <a
          href="https://jupyter-notebook.readthedocs.io/en/stable/public_server.html#securing-a-notebook-server"
          >Jupyter docs</a
        >.
      </p>
    </blockquote>
    <p>08/15/2019</p>
    <p>
      <a href="https://code.visualstudio.com/docs/python/jupyter-support"
        >Source</a
      >
    </p>
  </body>
</html>
